QAanalysis_tumor
12 June 2019
Contents
- 1 Quality assessment
- 1.1 Subset of samples: Tumor data
- 1.2 Sequencing depth among tumor samples
- 1.3 Gender among samples in tumor data
- 1.4 Distribution of expression levels among samples in tumor data
- 1.5 Distribution of expression levels among genes in tumor data
- 1.6 Filtering of lowly-expressed genes in tumor data
- 1.7 Normalization tumor data
- 1.8 MA plots tumor data
- 1.9 Batch Identification for tumor data.
- 1.10 Session information
1 Quality assessment
1.1 Subset of samples: Tumor data
From the total of samples before starting to analyse the data, we proceed to do a subset that only includes the tumor data. The reason of doing this is because we are interested also in the differential expression analysis between the different stages of the Colon Adenocarcinoma Cancer.
In order to create the subset, we looked for those samples that had “tumor” as their type and discarded those with “normal” type. We also discarded those samples that had no information about the stage of the tumor.
Although the data is categorized in diverse subgroups, we will work with the following ones: stage I, stage II, stage III and stage IV, being each one the sum of all the subcategories that share the same stage number:
filter_tumor_mask <- rownames(colData(coadse)[colData(coadse)$type == "tumor",])
coadse.tumor <- coadse[, filter_tumor_mask]
coadse.tumor <- coadse.tumor[, !is.na(coadse.tumor$ajcc_pathologic_tumor_stage)]
coadse.tumor <- coadse.tumor[, coadse.tumor$ajcc_pathologic_tumor_stage != "[Not Available]"]
coadse.tumor$ajcc_pathologic_tumor_stage <- gsub(x = coadse.tumor$ajcc_pathologic_tumor_stage, pattern = "A", replacement = "")
coadse.tumor$ajcc_pathologic_tumor_stage <- gsub(x = coadse.tumor$ajcc_pathologic_tumor_stage, pattern = "B", replacement = "")
coadse.tumor$ajcc_pathologic_tumor_stage <- gsub(x = coadse.tumor$ajcc_pathologic_tumor_stage, pattern = "C", replacement = "")
table(coadse.tumor$ajcc_pathologic_tumor_stage)
Stage I Stage II Stage III Stage IV
72 168 124 63 1.2 Sequencing depth among tumor samples
First of all, we want to collect an overview of the data that we are working with. Starting examining the sequencing depth by plotting the total number of reads mapped to the genome per sample.
CM.4748 AA.3696 AA.3837 AA.A02H AA.A010 AA.A00E A6.2679 AA.3672 AA.3524
1.2 3.0 4.5 5.1 5.5 5.7 5.9 6.6 6.8
AA.A01K AA.A004 AA.3947 AA.A01C CA.6717 D5.6536 AA.3976 AA.3506 AA.3841
7.3 7.4 7.6 8.0 8.0 8.1 8.3 8.6 8.6
A6.2682 AA.A03F AA.3667 AA.3664 AA.A029 AA.3519 AA.3812 AA.3949 AA.3956
9.0 9.6 9.9 10.2 10.2 11.0 11.0 11.2 11.3
AA.3842 AA.3848 AA.3875 AA.3930 AA.3955 AA.A01Q A6.3807 AA.3862 AA.3679
11.5 11.5 12.1 12.1 12.1 12.2 12.3 12.3 12.5
A6.A5ZU AA.3684 AA.3542 AA.3850 AA.A00F A6.A56B AA.3560 AA.3680 AA.3715
12.6 12.7 12.8 12.8 13.0 13.1 13.2 13.2 13.2
AA.A01F AA.A01T CM.4747 AA.3681 AZ.4313 AA.3495 AA.3531 AA.3521 AA.A01Z
13.4 13.4 13.4 13.5 13.5 13.6 13.6 13.7 13.7
AA.3861 AA.3502 AA.3970 AA.A00N AA.3950 AA.A024 AA.3666 AA.3530 AA.3710
13.9 14.1 14.1 14.2 14.3 14.3 14.5 14.6 14.6
AA.3517 AA.3821 AA.3972 AA.A00D AA.3971 AA.A00L A6.3808 AA.3544 AA.3548
14.7 14.7 14.7 14.7 14.9 14.9 15.0 15.0 15.0
AA.3845 AA.3975 AA.3852 AA.A01G AZ.4315 AA.3552 AA.3939 AA.3982 AA.3815
15.0 15.0 15.1 15.1 15.1 15.2 15.2 15.2 15.3
AA.A00R A6.A567 AA.3534 AA.3941 AA.3968 AA.3846 AA.3867 AA.3973 AA.A00K
15.3 15.4 15.4 15.4 15.4 15.5 15.5 15.6 15.7
CM.6169 A6.2683 AA.A00J AA.A00U AA.A02E DM.A1DB AA.3811 AA.A00W AA.A03J
15.7 15.8 15.8 15.9 15.9 15.9 16.0 16.0 16.0
AA.3833 AA.A017 AA.A00Q CM.4752 AA.A02F AA.3518 AA.3844 AA.3529 AA.3980
16.2 16.2 16.3 16.3 16.4 16.5 16.5 16.7 16.7
AA.3520 AY.4070 AA.3522 AA.3554 AA.3860 AA.3543 AA.A00O AZ.4308 A6.2676
16.8 16.8 17.0 17.0 17.0 17.1 17.1 17.1 17.2
AA.3516 AA.3989 AA.3858 AA.A00Z AY.4071 AA.3488 AA.3561 AA.3877 AA.A01V
17.2 17.2 17.3 17.3 17.3 17.4 17.4 17.4 17.4
AA.3525 AA.3492 AA.3851 AA.A00A AA.3514 AA.3549 AA.3866 AA.3984 AA.3986
17.5 17.6 17.6 17.6 17.7 17.7 17.7 17.7 17.7
AA.3994 AA.A022 AA.3870 AZ.4684 AA.3864 AA.3979 AA.A01X A6.2680 AA.3538
17.7 17.7 17.9 17.9 18.0 18.0 18.0 18.1 18.2
AA.3556 AA.3695 AA.3814 AA.3509 AA.A01S CM.4746 AA.3869 AA.A01P AA.A02W
18.2 18.2 18.3 18.4 18.5 18.5 18.7 18.7 18.7
AA.A02R CA.5256 G4.6304 AA.3510 AA.3527 AA.3952 AA.A01D AZ.4681 CM.5341
18.8 18.8 18.8 19.0 19.0 19.0 19.0 19.0 19.0
AA.3553 A6.2685 AA.3562 AA.3855 CM.4750 AA.A01R AZ.4614 AA.3831 AA.A02J
19.1 19.2 19.2 19.2 19.2 19.4 19.6 19.7 19.7
AA.A02O AA.3819 CK.4951 A6.4107 AA.3494 AA.3532 AA.3966 AA.3688 NH.A6GC
19.8 19.9 19.9 20.0 20.0 20.1 20.6 20.7 20.7
AA.A01I AZ.4615 A6.2671 A6.2678 AA.3818 A6.2681 AA.3678 AA.3555 AA.3854
20.9 21.0 21.1 21.1 21.2 21.4 21.4 21.5 21.6
4T.AA8H G4.6310 AA.3692 D5.6920 AA.3856 AA.3872 AA.3673 AA.3693 F4.6856
21.7 21.8 21.9 21.9 22.4 22.7 23.0 23.3 23.8
G4.6306 F4.6806 D5.6924 CM.6677 CM.5861 AA.3697 CM.5864 D5.6533 CA.5796
23.8 24.3 24.5 25.1 25.4 25.6 25.8 26.3 26.6
CM.5862 F4.6855 NH.A6GB G4.6323 A6.6141 CM.6166 F4.6808 G4.6320 AY.6196
26.8 26.9 27.0 27.4 28.0 28.0 28.2 28.3 29.0
NH.A8F8 AZ.5407 A6.A566 D5.6929 DM.A288 CM.5860 D5.6927 CM.5349 G4.6309
29.0 29.1 29.2 29.3 29.4 29.8 29.8 29.9 29.9
D5.5540 AZ.6598 CM.4743 F4.6805 CK.5912 G4.6298 D5.6930 G4.6315 A6.2675
30.0 30.2 30.2 30.3 30.4 30.6 30.8 30.8 31.0
AA.3663 A6.6142 CM.6165 G4.6311 G4.6295 F4.6460 F4.6461 CM.6674 CK.5916
31.1 31.3 31.5 31.5 31.6 31.8 31.8 31.9 32.3
G4.6321 AA.3662 DM.A280 G4.6293 CM.6167 G4.6588 AU.3779 CA.5255 QL.A97D
32.4 32.5 32.5 32.5 32.6 32.6 32.7 32.9 33.0
A6.4105 AD.5900 AZ.4323 AZ.6607 D5.6530 F4.6463 AA.3655 D5.5537 F4.6807
33.2 33.2 33.2 33.2 33.2 33.2 33.5 33.5 33.5
AA.3660 A6.5666 G4.6302 D5.6535 AZ.6599 RU.A8FL CK.5915 CK.6751 CM.6172
33.7 33.8 33.9 34.0 34.2 34.2 34.3 34.5 34.5
4N.A93T AY.A8YK G4.6627 3L.AA1B AM.5821 AZ.6605 CM.5344 D5.6531 DM.A28K
34.6 34.6 34.6 34.7 34.7 34.7 34.9 34.9 34.9
D5.5538 AD.6889 G4.6294 G4.6625 A6.6652 A6.6653 D5.6539 G4.6299 AD.A5EJ
35.3 35.4 35.4 35.5 35.6 35.6 35.6 35.7 36.3
D5.6538 NH.A50V AZ.5403 CA.6719 CM.6171 DM.A28G G4.6626 A6.6137 D5.6540
36.3 36.3 36.4 36.4 36.5 36.6 36.6 36.7 36.8
G4.6297 D5.6931 A6.6140 A6.6138 AZ.4616 DM.A28E CM.6675 CM.6680 A6.2686
36.9 37.0 37.1 37.3 37.4 37.5 37.6 37.7 37.8
CM.6161 CM.4751 CM.6676 CK.6746 D5.5539 NH.A50T A6.5667 AM.5820 CM.5863
37.9 38.0 38.0 38.1 38.1 38.1 38.2 38.2 38.3
G4.6586 A6.5660 AY.6386 D5.6926 DM.A282 AY.A54L AZ.6601 A6.5664 CK.4947
38.3 38.4 38.4 38.5 38.5 38.6 38.6 38.8 38.8
AA.3526 CA.6716 A6.6654 A6.6782 DM.A0XD F4.6459 A6.5662 D5.6537 G4.6314
39.0 39.0 39.2 39.3 39.4 39.4 39.5 39.5 39.5
D5.6932 DM.A1D4 G4.6628 DM.A28A AA.3496 AU.6004 D5.6532 AA.3685 CM.6163
39.6 39.6 39.6 39.7 39.8 39.8 39.8 39.9 39.9
F4.6570 CM.6170 D5.6928 DM.A28C CM.5348 NH.A5IV AA.3511 AA.3712 F4.6809
39.9 40.0 40.0 40.1 40.2 40.2 40.3 40.4 40.4
AA.A02K AZ.6606 AZ.6608 CK.4952 QG.A5YV QG.A5YX AY.6197 D5.6529 CM.6168
40.6 40.6 40.6 40.6 40.6 40.7 40.8 40.9 41.0
DM.A28H CM.6162 CK.5914 AZ.6600 AA.3713 A6.6651 CA.5797 CM.6679 CA.6718
41.0 41.1 41.6 41.9 42.0 42.3 42.5 42.5 42.7
CM.6678 AD.6548 AA.A02Y AD.A5EK AA.3675 CK.5913 D5.7000 DM.A1HA CA.6715
42.7 42.8 43.2 43.2 43.3 43.3 43.3 43.7 44.1
D5.6922 QG.A5YW AY.A71X F4.6569 AZ.4682 AA.3489 D5.6898 CM.6164 DM.A1HB
44.2 44.3 44.4 44.5 44.6 45.2 45.2 45.3 45.9
NH.A6GA AY.5543 CM.5868 G4.6322 D5.5541 CK.6748 QG.A5Z2 DM.A285 AD.6888
46.1 46.5 46.6 46.6 46.7 46.8 46.8 47.0 47.2
F4.6854 G4.6303 DM.A28F AD.6899 CA.5254 CK.6747 DM.A1D7 DM.A28M A6.A565
47.3 47.9 48.0 48.2 48.2 48.3 48.4 48.5 48.6
QG.A5Z1 D5.6541 AY.A69D DM.A1D9 WS.AB45 DM.A0XF CM.4744 NH.A50U DM.A1DA
48.9 49.0 49.1 49.4 49.4 50.6 50.7 50.7 50.9
CK.4948 D5.6534 D5.6923 DM.A1D0 CK.4950 A6.6649 F4.6703 DM.A0X9 A6.5657
51.5 51.7 52.0 52.0 52.5 52.7 53.9 54.5 56.7
G4.6307 DM.A1D6 A6.6648 F4.6704
56.9 62.7 63.1 67.5 Loading required package: ggplot2
Figure 1: Library sizes in increasing order
Looking at this plot, we can see two important details that we must take into consideration. The first one is that all four stages have different samples with different sequencing depth. The other one is that we observe that within the considerable differences in the sequencing depth of some samples, we can distinguish a some samples on the left part of the graph that present considerably low sequencing depths: this may generate problems in further analysis of the data and that is why we then wanted to remove these samples as shown below:
# Remove those samples with very low sequencing depth (<10)
coadse.tumor <- coadse.tumor[, dge.tumor$samples$lib.size >= 10*1e06]
dge.tumor <- DGEList(counts = assays(coadse.tumor)$counts, group = coadse.tumor$ajcc_pathologic_tumor_stage, genes = genes.tumor)By doing this, we have lost 21 samples from our dataset. Here we can observe again the same plot as before after having removed the samples with less coverage depth.
# coverage_filtered plot
par(mfrow = c(1, 1), mar = c(4, 5, 1, 1))
ord.tumor <- order(dge.tumor$sample$lib.size)
ggplot(as.data.frame(dge.tumor$samples), aes(x = row.names(dge.tumor$samples), y = dge.tumor$samples$lib.size[ord.tumor]/1000000, fill = dge.tumor$samples$group[ord.tumor])) + geom_bar(stat = "identity") + theme_bw() + theme(legend.title = element_blank(), axis.text.x=element_blank()) + ylab("Milions of reads") + xlab("Samples")
1.3 Gender among samples in tumor data
ord2 <- order(dge.tumor$sample$lib.size)
table(coadse.tumor$gender)
FEMALE MALE
194 212 barplot(dge.tumor$sample$lib.size[ord2]/1e+06, las = 1, ylab = "Millions of reads", xlab = "Samples",
col = c("cyan", "orange")[coadse.tumor$gender[ord2]], border = NA)
legend("topleft", c("Female", "Male"), fill = c("cyan", "orange"), inset = 0.01)
In the figure we can see that, as happened with the paired analysis, not only we have a similar number of female and male samples, but their sequencing depth is quite well equilibrated as we cannot clearly identify any defined clustering event.
1.4 Distribution of expression levels among samples in tumor data
As we have done in the paired analysis, we first run a within sample normalization and then we explore the distribution of the expression levels through all the samples in terms of logarithmic CPM units.
Figure 2: Non-parametric density distribution of expression profiles per sample
To provide a clearer visualization, we decided to divide the dataset into four smaller subsets, one for each stage. Figure ?? shows the expression levels in terms of logCPM for the different stages of tumor samples separately.
Figure 3: Non-parametric density distribution of expression profiles per sample
As we do not observe extreme differences in the distribution of the expression values in the different tumor stages, we decide to not exclude any sample from the dataset in this step.
1.5 Distribution of expression levels among genes in tumor data
We now want to look if there is any gene which has very low expression to exclude them from the dataset. In order to do so, we calculate the average expression of each gene for all the samples and plot their distribution in Figure 4.
Figure 4: Distribution of average expression level per gene
1.6 Filtering of lowly-expressed genes in tumor data
Those genes that have very low counts should be removed prior to further analysis because a gene must be expressed at some minimal level before it is likely to be translated into a protein and also because genes statistically they are very unlikely to be assessed as significantly differential expressed. So, such genes can therefore be removed from the analysis without any loss of information.
Then, to filter the lowly-expressed genes we calculate first a minimum CPM cutoff value of expression; 10 reads per million in the sample with lowest depth. We will keep also only those genes that have this minimum level of expresseion in at least as many samples as the smallest group of comparison.
cpmcutoff.tumor <- round(10/min(dge.tumor$sample$lib.size/1e+06), digits = 1)
cpmcutoff.tumor [1] 1sprintf("Cutoff: %s", cpmcutoff.tumor)[1] "Cutoff: 1"nsamplescutoff.tumor <- min(table(coadse.tumor$ajcc_pathologic_tumor_stage))
nsamplescutoff.tumor[1] 60mask.tumor <- rowSums(cpm(dge.tumor) > cpmcutoff.tumor) >= nsamplescutoff.tumor
coadse.tumor.filt <- coadse.tumor[mask.tumor, ]
dge.tumor.filt <- dge.tumor[mask.tumor, ]After the filtering of lowly-expressed genes, we have lost almost 6.000 genes.
dim(coadse.tumor)[1] 20115 406dim(coadse.tumor.filt)[1] 13569 406We can visually observe which genes have been left out from the datatset in Figure ??
par(mar = c(4, 5, 1, 1))
h <- hist(avgexp.tumor, xlab = expression("Expression level (" * log[2] * "CPM)"), main = "",
las = 1, col = "grey", cex.axis = 1.2, cex.lab = 1.5)
x <- cut(rowMeans(assays(coadse.tumor.filt)$logCPM), breaks = h$breaks)
lines(h$mids, table(x), type = "h", lwd = 10, lend = 1, col = "darkred")
legend("topright", c("All genes", "Filtered genes"), fill = c("grey", "darkred"))
1.7 Normalization tumor data
To make gene expression values comparable across samples, normalization step is needed to continue with our analysis. We estimate a normalization factor for each library using the Trimmed Mean of M-Values and apply it to our data.
dge.tumor.filt <- calcNormFactors(dge.tumor.filt)
assays(coadse.tumor.filt)$logCPM <- cpm(dge.tumor.filt, log=TRUE, normalized.lib.sizes=TRUE, prior.count=0.25)saveRDS(coadse.tumor.filt, file.path("results", "coadse.tumor.filt.rds"))
saveRDS(dge.tumor.filt, file.path("results", "dge.tumor.filt.rds"))1.8 MA plots tumor data
We now want to visualize the expression profiles of the normalized data for each tumor stage.
For Stage I –>
Figure 5: MA-plots of the tumor samples in Stage I
Figure 6: MA-plots of the tumor samples in Stage I
Figure 7: MA-plots of the tumor samples in Stage I
Figure 8: MA-plots of the tumor samples in Stage I
Figure 9: MA-plots of the tumor samples in Stage I
Figure 10: MA-plots of the tumor samples in Stage I
Figure 11: MA-plots of the tumor samples in Stage I
Figure 12: MA-plots of the tumor samples in Stage I
Figure 13: MA-plots of the tumor samples in Stage I
Figure 14: MA-plots of the tumor samples in Stage I
Figure 15: MA-plots of the tumor samples in Stage I
1.9 Batch Identification for tumor data.
As normalization of the data can not always remove completely the batch effect, the next step will be indeed the search of potential surrogate of batch effect indicators derived from different elements of the TCGA barcode of each sample.
tss.tumor <- substr(colnames(dge.tumor.filt), 6, 7)
table(data.frame(STAGE=coadse.tumor.filt$ajcc_pathologic_tumor_stage, TSS=tss.tumor)) TSS
STAGE 3L 4N 4T A6 AA AD AM AU AY AZ CA CK CM D5 DM F4 G4 NH QG QL RU
Stage I 1 0 0 2 31 3 0 1 3 3 0 4 9 5 0 4 2 0 1 1 0
Stage II 0 0 1 12 63 2 1 1 2 5 8 4 12 13 15 6 9 2 1 0 0
Stage III 0 1 0 15 37 2 0 0 3 4 1 4 10 11 8 5 10 3 3 0 1
Stage IV 0 0 0 7 24 0 1 0 2 8 0 2 5 1 1 1 5 3 0 0 0
TSS
STAGE WS
Stage I 0
Stage II 1
Stage III 0
Stage IV 0center.tumor <- substr(colnames(dge.tumor.filt), 27, 28)
table(data.frame(STAGE=coadse.tumor.filt$ajcc_pathologic_tumor_stage, CENTER=center.tumor)) CENTER
STAGE 07
Stage I 70
Stage II 158
Stage III 118
Stage IV 60samplevial.tumor <- substr(colnames(dge.tumor.filt), 14, 16)
table(data.frame(STAGE=coadse.tumor.filt$ajcc_pathologic_tumor_stage, SAMPLEVIAL=samplevial.tumor)) SAMPLEVIAL
STAGE 01A 01B
Stage I 70 0
Stage II 158 0
Stage III 117 1
Stage IV 58 2portionanalyte.tumor <- substr(colnames(dge.tumor.filt), 18, 20)
table(data.frame(STAGE=coadse.tumor.filt$ajcc_pathologic_tumor_stage, PORTIONANALYTE=portionanalyte.tumor)) PORTIONANALYTE
STAGE 01R 02R 03R 11R 12R 21R 23R 31R 33R 42R 43R 72R
Stage I 28 6 0 31 2 1 1 0 0 0 1 0
Stage II 68 12 0 59 4 14 0 0 0 1 0 0
Stage III 33 14 0 51 0 18 0 2 0 0 0 0
Stage IV 31 2 1 18 1 3 0 2 1 0 0 1plate.tumor <- substr(colnames(dge.tumor.filt), 22, 25)
table(data.frame(STAGE=coadse.tumor.filt$ajcc_pathologic_tumor_stage, PLATE=plate.tumor)) PLATE
STAGE 0821 0826 0905 1022 1113 1410 1653 1723 1774 1839 1873 1928 A002
Stage I 5 1 3 10 1 4 10 5 4 6 1 2 3
Stage II 10 3 12 15 1 11 13 14 13 16 0 12 6
Stage III 6 2 5 9 2 5 9 22 6 12 2 5 5
Stage IV 1 0 7 9 0 6 6 5 6 8 0 1 0
PLATE
STAGE A00A A083 A089 A155 A16W A180 A28H A32Y A32Z A37K A41B
Stage I 1 1 1 0 1 0 3 1 4 2 1
Stage II 5 1 0 8 6 0 3 3 2 2 2
Stage III 1 4 1 3 3 1 8 2 1 4 0
Stage IV 2 0 1 0 1 0 1 1 1 1 3Too look for potential surrogates of batch effect indicators we need the maximum information explained by the possible batch generator, that means no zeros along the different groups. In order to be able to identify a possible batch effect and looking the previous tables, we decided to continue our analysis focusing in ‘plate’ information. Then we decided to see graphically how batch might be confounding the outcome of interest by doing a hierarchical clustering.
logCPM <- cpm(dge.tumor.filt, log=TRUE, prior.count=3)
d <- as.dist(1-cor(logCPM, method="spearman"))
sampleClustering <- hclust(d)
batch <- as.integer(factor(plate.tumor))
sampleDendrogram <- as.dendrogram(sampleClustering, hang=0.1)
names(batch) <- colnames(coadse.tumor.filt)
outcome <- paste(substr(colnames(coadse.tumor.filt), 9, 12), as.character(coadse.tumor.filt$ajcc_pathologic_tumor_stage), sep="-")
names(outcome) <- colnames(coadse.tumor.filt)
sampleDendrogram <- dendrapply(sampleDendrogram,
function(x, batch, labels) {
if (is.leaf(x)) {
attr(x, "nodePar") <- list(lab.col=as.vector(batch[attr(x, "label")]))
attr(x, "label") <- as.vector(labels[attr(x, "label")])
}
x
}, batch, outcome)
plot(sampleDendrogram, main="Hierarchical clustering of samples")
legend("topright", paste("Batch", sort(unique(batch)), levels(factor(plate.tumor))), cex=0.75, fill=sort(unique(batch)))
Figure 16: Figure S6: Hierarchical clustering of the samples
As a result of this graph, we concluded that the hierarchical clustering can become quite useless as a graphical tool when you have a huge quantity of samples, so then we decided to change our approach and try to identify graphically the batch effect with a MDS plot.
col.stage <- c("blue","cyan", "orange", "purple")[factor(coadse.tumor.filt$ajcc_pathologic_tumor_stage)]
batch <- as.integer(factor(plate.tumor))
plotMDS(dge.tumor.filt, dim=c(3,4), col=col.stage, pch= batch, cex=2)
legend("topleft", legend=levels(factor(coadse.tumor.filt$ajcc_pathologic_tumor_stage)), col=col.stage ,pch=16, cex= 0.75)
legend("topright", legend=levels(factor(plate.tumor)),pch= batch, cex=0.75, ncol = 2)
With the MDSplot we are still not able to recognize any cluster of any group, so we can not conclude that ‘plate’ information could be a potential surrogate of batch effect.
1.10 Session information
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] geneplotter_1.60.0 annotate_1.60.1
[3] XML_3.98-1.19 AnnotationDbi_1.44.0
[5] lattice_0.20-38 ggplot2_3.1.1
[7] edgeR_3.24.3 limma_3.38.3
[9] SummarizedExperiment_1.12.0 DelayedArray_0.8.0
[11] BiocParallel_1.16.6 matrixStats_0.54.0
[13] Biobase_2.42.0 GenomicRanges_1.34.0
[15] GenomeInfoDb_1.18.2 IRanges_2.16.0
[17] S4Vectors_0.20.1 BiocGenerics_0.28.0
[19] knitr_1.22 BiocStyle_2.10.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 locfit_1.5-9.1 assertthat_0.2.1
[4] digest_0.6.18 R6_2.4.0 plyr_1.8.4
[7] RSQLite_2.1.1 evaluate_0.13 highr_0.8
[10] pillar_1.3.1 zlibbioc_1.28.0 rlang_0.3.4
[13] lazyeval_0.2.2 blob_1.1.1 Matrix_1.2-17
[16] rmarkdown_1.12 labeling_0.3 stringr_1.4.0
[19] RCurl_1.95-4.12 bit_1.1-14 munsell_0.5.0
[22] compiler_3.5.3 xfun_0.6 pkgconfig_2.0.2
[25] htmltools_0.3.6 tidyselect_0.2.5 tibble_2.1.1
[28] GenomeInfoDbData_1.2.0 bookdown_0.9 codetools_0.2-16
[31] crayon_1.3.4 dplyr_0.8.1 withr_2.1.2
[34] bitops_1.0-6 grid_3.5.3 xtable_1.8-4
[37] gtable_0.3.0 DBI_1.0.0 magrittr_1.5
[40] scales_1.0.0 KernSmooth_2.23-15 stringi_1.4.3
[43] XVector_0.22.0 RColorBrewer_1.1-2 tools_3.5.3
[46] bit64_0.9-7 glue_1.3.1 purrr_0.3.2
[49] yaml_2.2.0 colorspace_1.4-1 BiocManager_1.30.4
[52] memoise_1.1.0